def run(args):
if args.externals:
print(mvpa2.wtf(include=['externals']))
elif args.debug:
mvpa2.debug.print_registered()
elif not args.learner_warehouse is False:
from mvpa2.clfs.warehouse import clfswh
clfswh.print_registered(*args.learner_warehouse)
else:
print(mvpa2.wtf())
def run(args):
if args.externals:
print mvpa2.wtf(include=['externals'])
elif args.debug:
mvpa2.debug.print_registered()
elif not args.learner_warehouse is False:
from mvpa2.clfs.warehouse import clfswh
clfswh.print_registered(*args.learner_warehouse)
else:
print mvpa2.wtf()
def generate_testing_fmri_dataset(filename=None):
"""Helper to generate a dataset for regression testing of mvpa2/nibabel
Parameters
----------
filename : str
Filename of a dataset file to store. If not provided, it is composed
using :func:`get_testing_fmri_dataset_filename`
Returns
-------
Dataset, string
Generated dataset, filename to the HDF5 where it was stored
"""
import mvpa2
from mvpa2.base.hdf5 import h5save
from mvpa2.datasets.sources import load_example_fmri_dataset
# Load our sample dataset
ds_full = load_example_fmri_dataset(name='1slice', literal=False)
# Subselect a small "ROI"
ds = ds_full[20:23, 10:14]
# collect all versions/dependencies for possible need to troubleshoot later
ds.a['wtf'] = mvpa2.wtf()
ds.a['versions'] = mvpa2.externals.versions
# save to a file identified by version of PyMVPA and nibabel and hash of
# all other versions
out_filename = filename or get_testing_fmri_dataset_filename()
h5save(out_filename, ds, compression=9)
# ATM it produces >700kB .hdf5 which is this large because of
# the ds.a.mapper with both Flatten and StaticFeatureSelection occupying
# more than 190kB each, with ds.a.mapper as a whole generating 570kB file
# Among those .ca seems to occupy notable size, e.g. 130KB for the FlattenMapper
# even though no heavy storage is really needed for any available value --
# primarily all is meta-information embedded into hdf5 to describe our things
return ds, out_filename
def generate_testing_fmri_dataset(filename=None):
"""Helper to generate a dataset for regression testing of mvpa2/nibabel
Parameters
----------
filename : str
Filename of a dataset file to store. If not provided, it is composed
using :func:`get_testing_fmri_dataset_filename`
Returns
-------
Dataset, string
Generated dataset, filename to the HDF5 where it was stored
"""
import mvpa2
from mvpa2.base.hdf5 import h5save
from mvpa2.datasets.sources import load_example_fmri_dataset
# Load our sample dataset
ds_full = load_example_fmri_dataset(name='1slice', literal=False)
# Subselect a small "ROI"
ds = ds_full[20:23, 10:14]
# collect all versions/dependencies for possible need to troubleshoot later
ds.a['wtf'] = mvpa2.wtf()
ds.a['versions'] = mvpa2.externals.versions
# save to a file identified by version of PyMVPA and nibabel and hash of
# all other versions
out_filename = filename or get_testing_fmri_dataset_filename()
h5save(out_filename, ds, compression=9)
# ATM it produces >700kB .hdf5 which is this large because of
# the ds.a.mapper with both Flatten and StaticFeatureSelection occupying
# more than 190kB each, with ds.a.mapper as a whole generating 570kB file
# Among those .ca seems to occupy notable size, e.g. 130KB for the FlattenMapper
# even though no heavy storage is really needed for any available value --
# primarily all is meta-information embedded into hdf5 to describe our things
return ds, out_filename
def teardown_module(module, verbosity=None):
"tear down test fixtures"
verbosity = _get_verbosity(verbosity)
# restore warning handlers
warning.maxcount = _sys_settings['maxcount']
if verbosity < 3:
# restore warning handlers
warning.handlers = _sys_settings['handlers']
if verbosity < 4:
# restore numpy settings
np.seterr(**_sys_settings['np_errsettings'])
if cfg.getboolean('tests', 'wtf', default='no'):
sys.stderr.write(str(wtf()))
def teardown_module(module, verbosity=None):
"tear down test fixtures"
verbosity = _get_verbosity(verbosity)
# restore warning handlers
warning.maxcount = _sys_settings['maxcount']
if verbosity < 3:
# restore warning handlers
warning.handlers = _sys_settings['handlers']
if verbosity < 4:
# restore numpy settings
np.seterr(**_sys_settings['np_errsettings'])
if cfg.getboolean('tests', 'wtf', default='no'):
sys.stderr.write(str(wtf()))
